Results for “instruction-resolution”

LLaVA-NeXT: Improved Reasoning, OCR, and World Knowledge

Merges mature lessons from a domain memory file into its instruction file, preserving knowledge with minimal redundancy.

Teach testing progressively through structured sessions. Use when user says "lets learn testing" or "I want to study test practices"

Curriculum Learning

Monta o roteiro da audiencia de instrucao e julgamento — AIJ (CPC 358-368): ordem das provas orais (361 — perito/assistentes, depoimento das partes, testemunhas do autor e do reu), inquiricao de testemunhas (perguntas pelas partes — 459), depoimento pessoal (385-388), pena de confesso, contradita, e as alegacoes finais orais (364) com prazo de 20 minutos. Use quando o operador disser audiencia de instrucao, AIJ, instrucao e julgamento, oitiva de testemunhas, perguntas para a testemunha, depoimento pessoal, alegacoes finais orais, ou houver AIJ designada para preparar.

Operates as an autonomous system that loads context from index files, follows goal-driven principles, and uses available agents and skills to execute decisions.

Provides a structured methodology for solving nontrivial, ambiguous problems by framing outcomes, selecting evidence, routing specialists, choosing options, and verifying results.

Improved Baselines with Visual Instruction Tuning

Visual Instruction Tuning

Instruction Tuning with GPT-4

No Robots: A Dataset of Personally Written Instructions

Scaling Instruction-Finetuned Language Models

Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Fires at session end. Requires consent on first run.

One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.

Designs and documents structured incident response playbooks aligned with NIST SP 800-61r3 and SANS PICERL frameworks, covering playbook structure, decision trees, escalation criteria, RACI matrices, and SOAR integration.

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

Master orchestrator to resolve a PR by diagnosing state and delegating to specialized skills.

Idefics2: An 8B Parameters Multimodal Model

SVIT: Scaling up Visual Instruction Tuning

Mantis: Interleaved Multi-Image Instruction Tuning

Execution discipline with agency, instinct detection, critical safety rules, trust layer, and error resilience. v2.7 adds timeout handling, checkpointing, and decision logging.

Diagnoses and fixes build, type, and runtime errors with minimal changes, covering TypeScript, PyTorch, and other build systems.

Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.

Use when two or more agents produce conflicting outputs, recommendations, or decisions. This skill provides a structured process for resolving disagreements between agents, ensuring conflicts are resolved fairly with clear reasoning and documented decisions.

当需要克服拖延、激发最大行动力以完成一项重大挑战或转型时

Reconcile canonical declarative documents under docs/ with verified implementation discoveries after a FULLY_IMPLEMENTED moonspec-verify verdict. Use when an orchestration run must decide whether verified discoveries show the owning canonical document is impossible, unclear, or inconsistent, apply the smallest correct doc update, or escalate ambiguous authority conflicts and deliberate divergences instead of editing.

Outcome Eval

Imported skill tsconfig from vercel

Record, search, and prune per-project learnings in .claude/learnings.jsonl — typed, confidence-scored, searchable across sessions

Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

Dolphins: Multimodal Language Model for Driving

Matryoshka Representation Learning

Design MOOCs with scalable engagement. TRIGGERS - Use when user needs help with mooc-design related tasks.

Guide users through learning new skills or concepts with structured lessons, reference materials, and progress tracking within a dedicated workspace.